Advancing Human-Machine Teaming: Concepts, Challenges, and Applications
This work addresses the problem of improving collaboration between humans and machines in domains such as defense and healthcare, but it is incremental as a survey that synthesizes existing concepts.
This survey tackles the problem of advancing Human-Machine Teaming (HMT) by presenting a comprehensive taxonomy and analyzing theoretical models, interdisciplinary methodologies, and key challenges like explainability and role allocation, with the result of laying a foundation for resilient, ethical, and scalable HMT systems.
Human-Machine Teaming (HMT) is revolutionizing collaboration across domains such as defense, healthcare, and autonomous systems by integrating AI-driven decision-making, trust calibration, and adaptive teaming. This survey presents a comprehensive taxonomy of HMT, analyzing theoretical models, including reinforcement learning, instance-based learning, and interdependence theory, alongside interdisciplinary methodologies. Unlike prior reviews, we examine team cognition, ethical AI, multi-modal interactions, and real-world evaluation frameworks. Key challenges include explainability, role allocation, and scalable benchmarking. We propose future research in cross-domain adaptation, trust-aware AI, and standardized testbeds. By bridging computational and social sciences, this work lays a foundation for resilient, ethical, and scalable HMT systems.